विधियों की तुलना करें

	क्रिगिंग स्थानिक अंतर्वेशन (Kriging Spatial Interpolation)	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	स्थानिक विश्लेषण	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1963	2019
प्रवर्तक	Georges Matheron (formalised geostatistics)	Wooldridge (textbook treatment); classical least squares
प्रकार	Geostatistical spatial interpolation	Linear regression
मौलिक स्रोत	Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	5	5
सारांश	Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).